{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 22: Logarithmic Mean Risk Optimization (Kelly Criterion)\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  27 of 27 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2000-01-01'\n",
    "end = '2019-12-31'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['AIG', 'AKAM', 'ALXN', 'AMT', 'APA', 'BA', 'BAX', 'BKNG',\n",
    "          'BMY', 'CMCSA', 'CNP', 'CPB', 'DE', 'MO', 'MSFT', 'NI',\n",
    "          'NKTR', 'NTAP', 'PCAR', 'PSA', 'REGN', 'SBAC', 'SEE', 'T',\n",
    "          'TGT', 'TMO', 'TTWO']\n",
    "\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(239, 27)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>ALXN</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>-15.2694%</td>\n",
       "      <td>4.8670%</td>\n",
       "      <td>94.8571%</td>\n",
       "      <td>37.2822%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-16.7100%</td>\n",
       "      <td>-14.6771%</td>\n",
       "      <td>-3.5560%</td>\n",
       "      <td>-13.5849%</td>\n",
       "      <td>-7.2464%</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0449%</td>\n",
       "      <td>4.6443%</td>\n",
       "      <td>-2.7549%</td>\n",
       "      <td>358.8832%</td>\n",
       "      <td>33.6082%</td>\n",
       "      <td>-11.4699%</td>\n",
       "      <td>-11.9534%</td>\n",
       "      <td>-10.2961%</td>\n",
       "      <td>-9.7473%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>23.8863%</td>\n",
       "      <td>-38.4450%</td>\n",
       "      <td>-18.1818%</td>\n",
       "      <td>0.2538%</td>\n",
       "      <td>36.5171%</td>\n",
       "      <td>2.3689%</td>\n",
       "      <td>15.0230%</td>\n",
       "      <td>43.0168%</td>\n",
       "      <td>-0.2184%</td>\n",
       "      <td>3.1250%</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.3179%</td>\n",
       "      <td>16.1103%</td>\n",
       "      <td>-3.8081%</td>\n",
       "      <td>-47.6770%</td>\n",
       "      <td>8.6420%</td>\n",
       "      <td>9.3082%</td>\n",
       "      <td>11.5894%</td>\n",
       "      <td>26.6949%</td>\n",
       "      <td>30.4000%</td>\n",
       "      <td>6.5327%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-30</th>\n",
       "      <td>0.1713%</td>\n",
       "      <td>-38.5154%</td>\n",
       "      <td>-36.0215%</td>\n",
       "      <td>-5.6962%</td>\n",
       "      <td>-2.6382%</td>\n",
       "      <td>4.9587%</td>\n",
       "      <td>8.5878%</td>\n",
       "      <td>-20.9375%</td>\n",
       "      <td>-7.8650%</td>\n",
       "      <td>-5.4546%</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.6496%</td>\n",
       "      <td>-4.8750%</td>\n",
       "      <td>6.5476%</td>\n",
       "      <td>-3.3827%</td>\n",
       "      <td>-7.6705%</td>\n",
       "      <td>2.4165%</td>\n",
       "      <td>4.5795%</td>\n",
       "      <td>-10.9532%</td>\n",
       "      <td>-4.9080%</td>\n",
       "      <td>-27.3585%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-31</th>\n",
       "      <td>2.6661%</td>\n",
       "      <td>-32.4905%</td>\n",
       "      <td>-16.6667%</td>\n",
       "      <td>-20.2685%</td>\n",
       "      <td>25.6129%</td>\n",
       "      <td>-1.2138%</td>\n",
       "      <td>2.1113%</td>\n",
       "      <td>-39.7233%</td>\n",
       "      <td>5.0060%</td>\n",
       "      <td>-3.6859%</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.6796%</td>\n",
       "      <td>-11.3613%</td>\n",
       "      <td>-0.2793%</td>\n",
       "      <td>-28.6652%</td>\n",
       "      <td>-8.3077%</td>\n",
       "      <td>0.6742%</td>\n",
       "      <td>-0.2853%</td>\n",
       "      <td>-5.6896%</td>\n",
       "      <td>-4.1936%</td>\n",
       "      <td>-5.8442%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-06-30</th>\n",
       "      <td>4.3865%</td>\n",
       "      <td>77.8792%</td>\n",
       "      <td>92.2689%</td>\n",
       "      <td>12.2896%</td>\n",
       "      <td>-3.3385%</td>\n",
       "      <td>7.0399%</td>\n",
       "      <td>5.7331%</td>\n",
       "      <td>-0.3689%</td>\n",
       "      <td>5.7888%</td>\n",
       "      <td>3.4941%</td>\n",
       "      <td>...</td>\n",
       "      <td>24.6854%</td>\n",
       "      <td>-5.2239%</td>\n",
       "      <td>6.1300%</td>\n",
       "      <td>46.3190%</td>\n",
       "      <td>39.4296%</td>\n",
       "      <td>-6.4732%</td>\n",
       "      <td>0.7154%</td>\n",
       "      <td>-7.4775%</td>\n",
       "      <td>13.4680%</td>\n",
       "      <td>33.7931%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AIG      AKAM      ALXN       AMT      APA        BA  \\\n",
       "Date                                                                    \n",
       "2000-02-29 -15.2694%   4.8670%  94.8571%  37.2822%  0.0000% -16.7100%   \n",
       "2000-03-31  23.8863% -38.4450% -18.1818%   0.2538% 36.5171%   2.3689%   \n",
       "2000-04-30   0.1713% -38.5154% -36.0215%  -5.6962% -2.6382%   4.9587%   \n",
       "2000-05-31   2.6661% -32.4905% -16.6667% -20.2685% 25.6129%  -1.2138%   \n",
       "2000-06-30   4.3865%  77.8792%  92.2689%  12.2896% -3.3385%   7.0399%   \n",
       "\n",
       "                 BAX      BKNG       BMY    CMCSA  ...      NTAP      PCAR  \\\n",
       "Date                                               ...                       \n",
       "2000-02-29 -14.6771%  -3.5560% -13.5849% -7.2464%  ...  88.0449%   4.6443%   \n",
       "2000-03-31  15.0230%  43.0168%  -0.2184%  3.1250%  ... -12.3179%  16.1103%   \n",
       "2000-04-30   8.5878% -20.9375%  -7.8650% -5.4546%  ... -10.6496%  -4.8750%   \n",
       "2000-05-31   2.1113% -39.7233%   5.0060% -3.6859%  ... -12.6796% -11.3613%   \n",
       "2000-06-30   5.7331%  -0.3689%   5.7888%  3.4941%  ...  24.6854%  -5.2239%   \n",
       "\n",
       "                PSA      REGN     SBAC       SEE         T       TGT      TMO  \\\n",
       "Date                                                                            \n",
       "2000-02-29 -2.7549% 358.8832% 33.6082% -11.4699% -11.9534% -10.2961% -9.7473%   \n",
       "2000-03-31 -3.8081% -47.6770%  8.6420%   9.3082%  11.5894%  26.6949% 30.4000%   \n",
       "2000-04-30  6.5476%  -3.3827% -7.6705%   2.4165%   4.5795% -10.9532% -4.9080%   \n",
       "2000-05-31 -0.2793% -28.6652% -8.3077%   0.6742%  -0.2853%  -5.6896% -4.1936%   \n",
       "2000-06-30  6.1300%  46.3190% 39.4296%  -6.4732%   0.7154%  -7.4775% 13.4680%   \n",
       "\n",
       "                TTWO  \n",
       "Date                  \n",
       "2000-02-29   0.0000%  \n",
       "2000-03-31   6.5327%  \n",
       "2000-04-30 -27.3585%  \n",
       "2000-05-31  -5.8442%  \n",
       "2000-06-30  33.7931%  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "#Y = data[assets].pct_change().dropna()\n",
    "Y = data[assets].copy()\n",
    "Y = Y.resample('M').last().pct_change().dropna()\n",
    "print(Y.shape)\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Logarithmic Mean Variance Portfolios\n",
    "\n",
    "### 2.1 Calculating the portfolio that maximizes Risk Adjusted Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arithmetic</th>\n",
       "      <th>Log Approx</th>\n",
       "      <th>Log Exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALXN</th>\n",
       "      <td>3.3014%</td>\n",
       "      <td>3.3321%</td>\n",
       "      <td>3.3409%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.6165%</td>\n",
       "      <td>0.5924%</td>\n",
       "      <td>0.5881%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAX</th>\n",
       "      <td>0.6798%</td>\n",
       "      <td>1.2922%</td>\n",
       "      <td>1.3223%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKNG</th>\n",
       "      <td>2.9275%</td>\n",
       "      <td>2.8005%</td>\n",
       "      <td>2.8085%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0017%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>0.0923%</td>\n",
       "      <td>0.5654%</td>\n",
       "      <td>0.6307%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>3.3166%</td>\n",
       "      <td>3.3503%</td>\n",
       "      <td>3.2996%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>28.5731%</td>\n",
       "      <td>28.0809%</td>\n",
       "      <td>28.1082%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NI</th>\n",
       "      <td>6.1470%</td>\n",
       "      <td>6.3314%</td>\n",
       "      <td>6.3678%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTR</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTAP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCAR</th>\n",
       "      <td>7.2620%</td>\n",
       "      <td>7.2307%</td>\n",
       "      <td>7.2799%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>30.1037%</td>\n",
       "      <td>30.0239%</td>\n",
       "      <td>29.9771%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGN</th>\n",
       "      <td>1.3064%</td>\n",
       "      <td>1.2040%</td>\n",
       "      <td>1.1981%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBAC</th>\n",
       "      <td>2.0041%</td>\n",
       "      <td>1.7787%</td>\n",
       "      <td>1.7956%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>9.1721%</td>\n",
       "      <td>9.1473%</td>\n",
       "      <td>9.0319%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTWO</th>\n",
       "      <td>4.4975%</td>\n",
       "      <td>4.2681%</td>\n",
       "      <td>4.2513%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arithmetic  Log Approx  Log Exact\n",
       "AIG       0.0000%     0.0000%    0.0000%\n",
       "AKAM      0.0000%     0.0000%    0.0000%\n",
       "ALXN      3.3014%     3.3321%    3.3409%\n",
       "AMT       0.0000%     0.0001%    0.0000%\n",
       "APA       0.0000%     0.0000%    0.0000%\n",
       "BA        0.6165%     0.5924%    0.5881%\n",
       "BAX       0.6798%     1.2922%    1.3223%\n",
       "BKNG      2.9275%     2.8005%    2.8085%\n",
       "BMY       0.0000%     0.0017%    0.0000%\n",
       "CMCSA     0.0000%     0.0000%    0.0000%\n",
       "CNP       0.0923%     0.5654%    0.6307%\n",
       "CPB       0.0000%     0.0000%    0.0000%\n",
       "DE        3.3166%     3.3503%    3.2996%\n",
       "MO       28.5731%    28.0809%   28.1082%\n",
       "MSFT      0.0000%     0.0000%    0.0000%\n",
       "NI        6.1470%     6.3314%    6.3678%\n",
       "NKTR      0.0000%     0.0000%    0.0000%\n",
       "NTAP      0.0000%     0.0000%    0.0000%\n",
       "PCAR      7.2620%     7.2307%    7.2799%\n",
       "PSA      30.1037%    30.0239%   29.9771%\n",
       "REGN      1.3064%     1.2040%    1.1981%\n",
       "SBAC      2.0041%     1.7787%    1.7956%\n",
       "SEE       0.0000%     0.0000%    0.0000%\n",
       "T         0.0000%     0.0000%    0.0000%\n",
       "TGT       0.0000%     0.0001%    0.0000%\n",
       "TMO       9.1721%     9.1473%    9.0319%\n",
       "TTWO      4.4975%     4.2681%    4.2513%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "port.solvers = ['MOSEK']\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w_1 = port.optimization(model=model, rm=rm, obj=obj, kelly=False, rf=rf, l=l, hist=hist)\n",
    "w_2 = port.optimization(model=model, rm=rm, obj=obj, kelly='approx', rf=rf, l=l, hist=hist)\n",
    "w_3 = port.optimization(model=model, rm=rm, obj=obj, kelly='exact', rf=rf, l=l, hist=hist)\n",
    "\n",
    "w = pd.concat([w_1, w_2, w_3], axis=1)\n",
    "w.columns = ['Arithmetic', 'Log Approx', 'Log Exact']\n",
    "\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "w.plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Adjusted Return:\n",
      "Arithmetic 1.2777609920263315\n",
      "Log Approx 1.2780908644682214\n",
      "Log Exact 1.2780962788021772\n"
     ]
    }
   ],
   "source": [
    "import riskfolio.RiskFunctions as rk\n",
    "\n",
    "returns = port.returns\n",
    "cov = port.cov\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Risk Adjusted Return:\")\n",
    "print(\"Arithmetic\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Approx\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_3.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_3, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Exact\", (y/x).item() * 12**0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>ALXN</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4605%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9714%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.4388%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.7157%</td>\n",
       "      <td>4.2748%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0476%</td>\n",
       "      <td>21.9683%</td>\n",
       "      <td>0.8016%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.8771%</td>\n",
       "      <td>2.8017%</td>\n",
       "      <td>1.1746%</td>\n",
       "      <td>1.8656%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.7487%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.9647%</td>\n",
       "      <td>1.8393%</td>\n",
       "      <td>4.6034%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.3799%</td>\n",
       "      <td>27.6810%</td>\n",
       "      <td>0.8600%</td>\n",
       "      <td>0.2902%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.6299%</td>\n",
       "      <td>1.0729%</td>\n",
       "      <td>6.9863%</td>\n",
       "      <td>3.0699%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.2927%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.3181%</td>\n",
       "      <td>2.2990%</td>\n",
       "      <td>2.5943%</td>\n",
       "      <td>1.4850%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.0998%</td>\n",
       "      <td>29.7212%</td>\n",
       "      <td>1.0118%</td>\n",
       "      <td>1.4038%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.8272%</td>\n",
       "      <td>3.8982%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.0515%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.1497%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.4494%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.4394%</td>\n",
       "      <td>30.1212%</td>\n",
       "      <td>1.7689%</td>\n",
       "      <td>2.9496%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.5452%</td>\n",
       "      <td>5.7016%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.4221%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.3453%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.4373%</td>\n",
       "      <td>29.3928%</td>\n",
       "      <td>2.7135%</td>\n",
       "      <td>4.4188%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.0028%</td>\n",
       "      <td>7.5957%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AIG    AKAM    ALXN     AMT     APA      BA     BAX    BKNG     BMY  \\\n",
       "0 0.0000% 0.0000% 0.4605% 0.0000% 0.9714% 0.0000% 6.4388% 0.0000% 8.7157%   \n",
       "1 0.0000% 0.0000% 2.7487% 0.0000% 0.0000% 0.0000% 3.9647% 1.8393% 4.6034%   \n",
       "2 0.0000% 0.0000% 3.2927% 0.0000% 0.0000% 0.3181% 2.2990% 2.5943% 1.4850%   \n",
       "3 0.0000% 0.0000% 3.0515% 0.0000% 0.0000% 0.1497% 0.0000% 3.4494% 0.0000%   \n",
       "4 0.0000% 0.0000% 2.4221% 0.0000% 0.0000% 0.0000% 0.0000% 4.3453% 0.0000%   \n",
       "\n",
       "    CMCSA  ...    NTAP    PCAR      PSA    REGN    SBAC     SEE       T  \\\n",
       "0 4.2748%  ... 0.0000% 0.0476% 21.9683% 0.8016% 0.0000% 0.0000% 8.8771%   \n",
       "1 0.0000%  ... 0.0000% 5.3799% 27.6810% 0.8600% 0.2902% 0.0000% 1.6299%   \n",
       "2 0.0000%  ... 0.0000% 7.0998% 29.7212% 1.0118% 1.4038% 0.0000% 0.0000%   \n",
       "3 0.0000%  ... 0.0000% 7.4394% 30.1212% 1.7689% 2.9496% 0.0000% 0.0000%   \n",
       "4 0.0000%  ... 0.0000% 7.4373% 29.3928% 2.7135% 4.4188% 0.0000% 0.0000%   \n",
       "\n",
       "      TGT     TMO    TTWO  \n",
       "0 2.8017% 1.1746% 1.8656%  \n",
       "1 1.0729% 6.9863% 3.0699%  \n",
       "2 0.0000% 8.8272% 3.8982%  \n",
       "3 0.0000% 8.5452% 5.7016%  \n",
       "4 0.0000% 7.0028% 7.5957%  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 40 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, kelly=\"exact\", points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdbf4b9c460>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Log Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plf.plot_frontier(w_frontier=frontier,\n",
    "                       mu=mu,\n",
    "                       cov=cov,\n",
    "                       returns=returns,\n",
    "                       rm=rm,\n",
    "                       kelly=True,\n",
    "                       rf=rf,\n",
    "                       alpha=0.05,\n",
    "                       cmap='viridis',\n",
    "                       w=w_2,\n",
    "                       label=label,\n",
    "                       marker='*',\n",
    "                       s=16,\n",
    "                       c='r',\n",
    "                       height=6,\n",
    "                       width=10,\n",
    "                       ax=ax)\n",
    "\n",
    "y1 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy())) * 252 \n",
    "x1 = rk.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 252**0.5\n",
    "\n",
    "y2 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy())) * 252 \n",
    "x2 = rk.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 252**0.5\n",
    "\n",
    "ax.scatter(x=x1,\n",
    "           y=y1,\n",
    "           marker=\"^\",\n",
    "           s=8**2,\n",
    "           c=\"b\",\n",
    "           label=\"Max Risk Adjusted Arithmetic Return Portfolio\")\n",
    "ax.scatter(x=x2,\n",
    "           y=y2,\n",
    "           marker=\"v\",\n",
    "           s=8**2,\n",
    "           c=\"c\",\n",
    "           label=\"Max Risk Adjusted Approx Log Return Portfolio\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Logarithmic Mean EVaR Portfolios\n",
    "\n",
    "### 2.1 Calculating the portfolio that maximizes Risk Adjusted Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arithmetic</th>\n",
       "      <th>Log Approx</th>\n",
       "      <th>Log Exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALXN</th>\n",
       "      <td>18.4852%</td>\n",
       "      <td>13.4427%</td>\n",
       "      <td>13.9779%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAX</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.5982%</td>\n",
       "      <td>0.6052%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKNG</th>\n",
       "      <td>6.6657%</td>\n",
       "      <td>5.4005%</td>\n",
       "      <td>5.5344%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>8.9277%</td>\n",
       "      <td>10.7024%</td>\n",
       "      <td>10.5379%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>6.0619%</td>\n",
       "      <td>6.2407%</td>\n",
       "      <td>6.2591%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>14.3438%</td>\n",
       "      <td>16.2361%</td>\n",
       "      <td>16.0263%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NI</th>\n",
       "      <td>0.8013%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTR</th>\n",
       "      <td>2.8013%</td>\n",
       "      <td>5.1036%</td>\n",
       "      <td>4.8886%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTAP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCAR</th>\n",
       "      <td>1.2919%</td>\n",
       "      <td>1.4146%</td>\n",
       "      <td>1.3819%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>27.7636%</td>\n",
       "      <td>28.9550%</td>\n",
       "      <td>28.9018%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGN</th>\n",
       "      <td>9.4038%</td>\n",
       "      <td>3.0729%</td>\n",
       "      <td>3.6764%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBAC</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.0386%</td>\n",
       "      <td>1.7632%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTWO</th>\n",
       "      <td>3.4538%</td>\n",
       "      <td>6.7948%</td>\n",
       "      <td>6.4471%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arithmetic  Log Approx  Log Exact\n",
       "AIG       0.0000%     0.0000%    0.0000%\n",
       "AKAM      0.0000%     0.0000%    0.0000%\n",
       "ALXN     18.4852%    13.4427%   13.9779%\n",
       "AMT       0.0000%     0.0000%    0.0000%\n",
       "APA       0.0000%     0.0000%    0.0000%\n",
       "BA        0.0000%     0.0000%    0.0000%\n",
       "BAX       0.0000%     0.5982%    0.6052%\n",
       "BKNG      6.6657%     5.4005%    5.5344%\n",
       "BMY       8.9277%    10.7024%   10.5379%\n",
       "CMCSA     0.0000%     0.0000%    0.0000%\n",
       "CNP       6.0619%     6.2407%    6.2591%\n",
       "CPB       0.0000%     0.0000%    0.0000%\n",
       "DE        0.0000%     0.0000%    0.0000%\n",
       "MO       14.3438%    16.2361%   16.0263%\n",
       "MSFT      0.0000%     0.0000%    0.0000%\n",
       "NI        0.8013%     0.0000%    0.0000%\n",
       "NKTR      2.8013%     5.1036%    4.8886%\n",
       "NTAP      0.0000%     0.0000%    0.0000%\n",
       "PCAR      1.2919%     1.4146%    1.3819%\n",
       "PSA      27.7636%    28.9550%   28.9018%\n",
       "REGN      9.4038%     3.0729%    3.6764%\n",
       "SBAC      0.0000%     2.0386%    1.7632%\n",
       "SEE       0.0000%     0.0000%    0.0000%\n",
       "T         0.0000%     0.0000%    0.0000%\n",
       "TGT       0.0000%     0.0000%    0.0000%\n",
       "TMO       0.0000%     0.0000%    0.0000%\n",
       "TTWO      3.4538%     6.7948%    6.4471%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm = 'EVaR' # Risk measure\n",
    "\n",
    "w_1 = port.optimization(model=model, rm=rm, obj=obj, kelly=False, rf=rf, l=l, hist=hist)\n",
    "w_2 = port.optimization(model=model, rm=rm, obj=obj, kelly='approx', rf=rf, l=l, hist=hist)\n",
    "w_3 = port.optimization(model=model, rm=rm, obj=obj, kelly='exact', rf=rf, l=l, hist=hist)\n",
    "\n",
    "w = pd.concat([w_1, w_2, w_3], axis=1)\n",
    "w.columns = ['Arithmetic', 'Log Approx', 'Log Exact']\n",
    "\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pf7x0bE8ZohlNVs02bty4nDdvXrtPQ5IkaYWGT7uqy3LHgEnLPWfUiGHLrWtXCWA1z/33388OO+zQ7tNo2EsvvbR03pyzzz6bJ554gm984xttPqvG9fT7jog7M3Nc9+fagiNJkiRV3FVXXcVZZ53F4sWL2XrrrbnkkkvafUqlMcGRJEmSKm7ChAlMmDCh3afREhYZkCRJklQZJjiSJEnSalgbx7JX0ar+nk1wJEmSpFU0YMAAnnnmGZOckmUmzzzzDAMGDGh4H8fgSJIkSato6NChLFy4kKeeeqrdp1J5AwYM6FIOe2UaSnAi4mDgG0B/4LuZeXa37UcBU2uLLwEfy8zf1rZ1AC8CS4DFPZVykyRJkvqS9ddfnxEjRrT7NNSDlSY4EdEfOB84EFgIzI2IKzPzvrqn/QHYLzOfi4hDgBnAbnXbD8jMp5t43pIkSZK0nEbG4OwKPJyZj2TmK8Bs4PD6J2TmrZn5XG3xdqDxNiRJkiRJapJGEpwtgcfqlhfW1vXmOOCndcsJXBsRd0bEiat+ipIkSZLUmEbG4EQP63osFxERB1AkOHvXrd4rMx+PiDcC10XEA5l5Yw/7ngicCDBs2LAGTkuSJEmSumqkBWchsFXd8lDg8e5PiojRwHeBwzPzmc71mfl47d8ngTkUXd6Wk5kzMnNcZo4bMmRI469AkiRJkmoaSXDmAiMjYkREbAAcCVxZ/4SIGAb8N/DhzPxd3fqBETGo8zFwEHBPs05ekiRJkuqttItaZi6OiJOBn1GUib4oM++NiCm17dOBzwGbAd+KCFhWDnpzYE5t3XrApZl5TSmvRJIkSdI6r6F5cDLzauDqbuum1z0+Hji+h/0eAcas4TlKkiRJUkMa6aImSZIkSX2CCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlmOBIkiRJqgwTHEmSJEmVYYIjSZIkqTJMcCRJkiRVhgmOJEmSpMowwZEkSZJUGSY4kiRJkirDBEeSJElSZZjgSJIkSaoMExxJkiRJlWGCI0mSJKkyTHAkSZIkVYYJjiRJkqTKMMGRJEmSVBkmOJIkSZIqwwRHkiRJUmWY4EiSJEmqDBMcSZIkSZVhgiNJkiSpMkxwJEmSJFWGCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlmOBIkiRJqgwTHEmSJEmVYYIjSZIkqTJMcCRJkiRVhgmOJEmSpMowwZEkSZJUGSY4kiRJkirDBEeSJElSZZjgSJIkSaoMExxJkiRJlWGCI0mSJKkyTHAkSZIkVYYJjiRJkqTKMMGRJEmSVBkmOJIkSZIqwwRHkiRJUmU0lOBExMER8WBEPBwR03rYflRE3F37uTUixjS6ryRJkiQ1y0oTnIjoD5wPHALsCEyMiB27Pe0PwH6ZORr4AjBjFfaVJEmSpKZopAVnV+DhzHwkM18BZgOH1z8hM2/NzOdqi7cDQxvdV5IkSZKapZEEZ0vgsbrlhbV1vTkO+Olq7itJkiRJq229Bp4TPazLHp8YcQBFgrP3aux7InAiwLBhwxo4LUmSJEnqqpEWnIXAVnXLQ4HHuz8pIkYD3wUOz8xnVmVfgMyckZnjMnPckCFDGjl3SZIkSeqikQRnLjAyIkZExAbAkcCV9U+IiGHAfwMfzszfrcq+kiRJktQsK+2ilpmLI+Jk4GdAf+CizLw3IqbUtk8HPgdsBnwrIgAW11pjety3pNciSZIkaR3XyBgcMvNq4Opu66bXPT4eOL7RfSVJkiSpDA1N9ClJkiRJfYEJjiRJkqTKMMGRJEmSVBkmOJIkSZIqwwRHkiRJUmWY4EiSJEmqDBMcSZIkSZVhgiNJkiSpMkxwJEmSJFWGCY4kSZKkyjDBkSRJklQZ67X7BNSz4dOu6rLccfZhbToTSZIkqe+wBUeSJElSZZjgSJIkSaoMExxJkiRJlWGCI0mSJKkyTHAkSZIkVYYJjiRJkqTKMMGRJEmSVBkmOJIkSZIqwwRHkiRJUmWY4EiSJEmqDBMcSZIkSZVhgiNJkiSpMkxwJEmSJFWGCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlmOBIkiRJqgwTHEmSJEmVYYIjSZIkqTJMcCRJkiRVhgmOJEmSpMowwZEkSZJUGeu1+wQkSerV6YN7WPdC689DktRn2IIjSZIkqTJswZEk9SmjZo7qsrxg8oI2nYkkaW1kC44kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlmOBIkiRJqgwTHEmSJEmV0VCCExEHR8SDEfFwREzrYftbIuK2iPh7RHyq27aOiFgQEfMjYl6zTlySJEmSulvpPDgR0R84HzgQWAjMjYgrM/O+uqc9C3wCeF8vhzkgM59ew3OVJEmSpBVqpAVnV+DhzHwkM18BZgOH1z8hM5/MzLnAqyWcoyRJkiQ1pJEEZ0vgsbrlhbV1jUrg2oi4MyJOXJWTkyRJkqRVsdIuakD0sC5XIcZemfl4RLwRuC4iHsjMG5cLUiQ/JwIMGzZsFQ4vSZIkSYVGWnAWAlvVLQ8FHm80QGY+Xvv3SWAORZe3np43IzPHZea4IUOGNHp4SZIkSVqqkQRnLjAyIkZExAbAkcCVjRw8IgZGxKDOx8BBwD2re7KSJEmStCIr7aKWmYsj4mTgZ0B/4KLMvDciptS2T4+ILYB5wOuB1yLiFGBH4A3AnIjojHVpZl5TyiuRJEmStM5rZAwOmXk1cHW3ddPrHv+Zoutad38BxqzJCUqSJElSoxqa6FOSJEmS+gITHEmSJEmV0VAXNUmSyjZ82lXLresY0IYTkST1abbgSJIkSaoMExxJkiRJlWGCI0mSJKkyHIPTh42aOWq5dQsmL2jDmUiSJElrB1twJEmSJFWGCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlWEWtrzh98PLrRgxr/XlIkiRJazFbcCRJkiRVhgmOJEmSpMowwZEkSZJUGSY4kiRJkirDBEeSJElSZZjgSJIkSaoMExxJkiRJlWGCI0mSJKkyTHAkSZIkVYYJjiRJkqTKMMGRJEmSVBkmOJIkSZIqwwRHkiRJUmWY4EiSJEmqDBMcSZIkSZVhgiNJkiSpMkxwJEmSJFWGCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlmOBIkiRJqgwTHEmSJEmVYYIjSZIkqTJMcCRJkiRVhgmOJEmSpMowwZEkSZJUGSY4kiRJkipjvXafgKQKO31wD+teaP15SJKkdYYtOJIkSZIqwxYcSS01auao5dYtmLygDWciSZKqyBYcSZIkSZXRUIITEQdHxIMR8XBETOth+1si4raI+HtEfGpV9pUkSZKkZllpghMR/YHzgUOAHYGJEbFjt6c9C3wC+Opq7CtJkiRJTdFIC86uwMOZ+UhmvgLMBg6vf0JmPpmZc4FXV3VfSZIkSWqWRhKcLYHH6pYX1tY1Yk32lSRJkqRV0kiCEz2sywaP3/C+EXFiRMyLiHlPPfVUg4eXJEmSpGUaSXAWAlvVLQ8FHm/w+A3vm5kzMnNcZo4bMmRIg4eXJEmSpGUaSXDmAiMjYkREbAAcCVzZ4PHXZF9JkiRJWiUrnegzMxdHxMnAz4D+wEWZeW9ETKltnx4RWwDzgNcDr0XEKcCOmfmXnvYt6bVIkiRJWsetNMEByMyrgau7rZte9/jPFN3PGtpXkiRJksrQ0ESfkiRJktQXNNSCI0mSpPYbPu2q5dZ1nH1YG85EWnvZgiNJkiSpMkxwJEmSJFWGCY4kSZKkyjDBkSRJklQZFhmQ1BQ9Dnwd0IYTkSRJ6zRbcCRJkiRVhgmOJEmSpMowwZEkSZJUGSY4kiRJkirDBEeSJElSZZjgSJIkSaoMExxJkiRJlWGCI0mSJKkyTHAkSZIkVYYJjiRJkqTKMMGRJEmSVBkmOJIkSZIqwwRHkiRJUmWY4EiSJEmqDBMcSZIkSZVhgiNJkiSpMkxwJEmSJFWGCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlmOBIkiRJqoz12n0CkiRJktYxpw/uYd0LTTm0LTiSJEmSKsMWHKmChk+7arl1HWcf1oYzkSRJai1bcCRJkiRVhi04kiRJktpu1MxRy61bMHnBKh/HFhxJkiRJlWELjiStqR4qwYwaMWy5datzF0qSJK0aW3AkSZIkVYYtOJK0CnqsUDegDSciSZJ6ZAuOJEmSpMowwZEkSZJUGSY4kiRJkirDBEeSJElSZZjgSJIkSaoMq6hJ67BmzRgsSZK0tjDBkdYVPUxGSQ+TUUqSJPVlDXVRi4iDI+LBiHg4Iqb1sD0i4pu17XdHxM512zoiYkFEzI+Iec08eUmSJEmqt9IWnIjoD5wPHAgsBOZGxJWZeV/d0w4BRtZ+dgMuqP3b6YDMfLppZy1JkiRJPWiki9quwMOZ+QhARMwGDgfqE5zDge9lZgK3R8Q/RMSbMvOJpp+xJElac927rZ7+QnvOQ5KarJEualsCj9UtL6yta/Q5CVwbEXdGxImre6KSJEmStDKNtOBED+tyFZ6zV2Y+HhFvBK6LiAcy88blghTJz4kAw4Y58FmSJEnSqmskwVkIbFW3PBR4vNHnZGbnv09GxByKLm/LJTiZOQOYATBu3LjuCZQkSSqRZeMlVUUjXdTmAiMjYkREbAAcCVzZ7TlXAkfXqqntDryQmU9ExMCIGAQQEQOBg4B7mnj+kiRJkrTUSltwMnNxRJwM/AzoD1yUmfdGxJTa9unA1cChwMPAIuDY2u6bA3MiojPWpZl5TdNfhSRJkiTR4ESfmXk1RRJTv2563eMETuphv0eAMWt4jpIkSZLUkIYm+pQkSZKkvsAER5IkSVJlmOBIkiRJqgwTHEmSJEmVYYIjSZIkqTJMcCRJkiRVhgmOJEmSpMowwZEkSZJUGSY4kiRJkipjvXafgCRJkppr1MxRy61bMHlBG85Eaj1bcCRJkiRVhi04kiRJkkozfNpVy63rGFBePFtwJEmSJFWGCY4kSZKkyjDBkSRJklQZjsGRJEnqy04fvPy6EcNafx7SWsIWHEmSJEmVYYIjSZIkqTLsoiZJUsW1ukSrJLWTLTiSJEmSKsMER5IkSVJlmOBIkiRJqgwTHEmSJEmVYYIjSZIkqTJMcCRJkiRVhgmOJEmSpMpwHhxJkiStmtMH97Duhdafh9QDW3AkSZIkVYYJjiRJkqTKsIuaJEmS1tiomaOWW7dg8oI2nInWdbbgSJIkSaoMExxJkiRJlWGCI0mSJKkyHIMjSZKqqYdSxqNGDFtuneNEpGoxwZEkSZUwfNpVXZY7BrTpRCS1lV3UJEmSJFWGCY4kSZKkyjDBkSRJklQZJjiSJEmSKsMER5IkSVJlWEVNkqQW6l7pC6Dj7MPacCaSVE224EiSJEmqDFtwpHZw8jlJkqRS2IIjSZIkqTIaSnAi4uCIeDAiHo6IaT1sj4j4Zm373RGxc6P7SpIkSVKzrDTBiYj+wPnAIcCOwMSI2LHb0w4BRtZ+TgQuWIV9JUmSJKkpGmnB2RV4ODMfycxXgNnA4d2eczjwvSzcDvxDRLypwX0lSZIkqSkaSXC2BB6rW15YW9fIcxrZV5IkSZKaIjJzxU+I+CDwrsw8vrb8YWDXzPx43XOuAs7KzJtry78APg1ss7J9645xIkX3NoDtgQdX4/W8AXh6NfZbE8Y0pjHXzpjrwms0pjGNaUxjVjfmuvAa1zTm1pk5pPvKRspELwS2qlseCjze4HM2aGBfADJzBjCjgfPpVUTMy8xxa3IMYxrTmNWIuS68RmMa05jGNGZ1Y64Lr7GsmI10UZsLjIyIERGxAXAkcGW351wJHF2rprY78EJmPtHgvpIkSZLUFCttwcnMxRFxMvAzoD9wUWbeGxFTatunA1cDhwIPA4uAY1e0bymvRJIkSdI6r5EuamTm1RRJTP266XWPEzip0X1LtEZd3IxpTGNWKua68BqNaUxjGtOY1Y25LrzGUmKutMiAJEmSJPUVjYzBkSRJkiovIhrq3aS1mwmOJEmSVLij3SegNWeCI0mSJBWi5QEjjmh1zKozwVEXtap3bRcR20bEZyPinjbE3ioiTivp2L3+zUXEP5QRsx1q5eLbLiIG1CYr1mqKiH+sezyi27bKfCivLde+domI7SPiOyUef0hEjFsbrnMRsXUbYq7f6phlqP0/7tjD+rdGxHKTLTYp5utXsG1YCSGHRMSpvf2UEA/gsyUdd4UiYqeI+F5EzIuIuRExMyJGlxzzkIi4MSKejoinIuJXEXFos+P0yX6GEfFWYNvMvLK2fA4wuLb5vMy8q4SYd/e2iaKQXNPfEBHxS6C3KhCZme9odkzgI8B5JRx3pSLiTcAEYBIwGjgLmNii2G8APliLtyUwp6RQ8yLiY5n5627xjwc+A2zT7IARsTewTWZ+r7b8I2DT2uYvZub1zY4JXBARdwBTM/P5Eo7fq4joDxxE8X/5LuAm4LIS4uxGUfllW2ABcFxm3tfsON1ijgT+FXgW+BrwHWBfihL9x2fm3BLCfhXYufb48rrHUHwo/3cJMYmInYBPAztSXAfvA/4jM3u7Fq+pll/7VvZlKTO/VkLM0RT/p/8HuAI4F/gWsBvwH82OV4t5PPBvwO+BERFxYufnd5kiYg+K6/mNmflk7bVPA/ah6yTkZcUP4ACKz7T3AJuXEOPoFW3vvO430bnABT2sH0pxbZrU5HgAN1C77kTEL7p997mCrtekZugPbEwbWnJaKSIOp7gWnFX7N4C3A5dHxKcy88clxDwB+CjFtX1ebfU44OyIGJqZTaum1icTHOBsiv+QTu8C/h/wOuBzwPtKiPkaxYfspcD/AH8rIUZ3n+ph3e4Ub4wnWxC/JWpv+IkUF8gfAscDP87MM0qOOwgYT3FB3o4iqdkmM4eWGPYTwIzOL//A1hRfLhZSfFEtwxnAx+uWtweOAQZSJFVlJDhvp3itd0TEFzLz+yXE6CIi9qX4vzyMog/1XsCIzFxUUsjzKf5GbwTeC5xDcS0q08XA94DXA78GTqF4D+9D8eV8txJiRi+Pe1puTsA2fPC2yaA2xPwOxRfU24CDgbsoPteOysyXS4p5CvDWzHwqIrYBfkDJk35HxFeAdwPzgakR8RPg/1IkWh8pOfZuFNei8RQ3k04CSukVAOzS0ylQJFRbUlwvmmlUZv6q+8rM/FlElJIg0/U6s+kKtjXLE5l5ZgnHXZG39HIjvbSb6MCZwIGZ2VG37rcRcT3w49pPs30S2Dszn61bd31EHALcTDPLRWdmn/sB5nVbvr3u8c0lxn0LxZfFu4D/pJjcdL0Wveb9gJ9T3I0+pMQ4i4G/9PDzIvCXkmK+AvwKGFe37pEW/E7/Vou7D8tKprcibn/gi8BzwGPAQSXHm9tt+b/rHt9ScuwdgRc63z9lvY8oEsRbgQ8Dg2rr/lDya7trRcslxZxf9/jh3raV9Tpb9ZqB3wLDe1g/HPhtSTFbfu1rx0/390ntGtS/5Jjt+Fu5DxhQe7xJ7Xo/suSYXwIeAn5BcaNus7KvQ93iB/Ahihbl/wJGlxDjdyvY9mDZ759WvJeA37Tq/6wu5r0UNzx7/Ckp5n2rs20NY96/OttW56evtuB0ueuVmfX9/d9YVtDMfAD4PPD5iJhAcWfky8BXyooZEZ2tUy8DX8rMX5YVq2ZBZr6t5Bjd/R+K7mFfi4jNKVpxWtFf+TPAkRR3My+NiP9qQUxY1hXuAuCdwISImJdd72g00z/UL2Rm/biJpneZ6BQRx1F0B/lX4PysXcFKcjlFy+0EYElE/Jjeu3c2yz90G4PSZTkzy+i69Vrd47+sYFszbRMRV1J8eep8TG15RO+7rZH1s+tdRQAys6PEsQwtv/ZFxOdWsDkz8wslhB0QEW9j2Z3vl4DRte5UZAldvIGhEfHN3pYz8xMlxPxb1lqkMvO5iHgwMx8qIU69E4EHKa7tP8nMlyOi9MkGa2WNjwH+haJl9wOZ+WBJ4R6KiEOzmMS9/hwOAR4pKeYba905o+4xteUyxv2U0f1/ZV7JzEdbHPPViBiWmX+sX1kbo7a4pJh/iYgxmfnbbjHHUNxMapo+OdFnbWzKtFx+HMPuwNmZuX9Jcbek+EI8nuLu+w+BOZn5Uknx5lL88X6FojtBF2V8EEXEb9qQ4NTHH0rxO55I0eVwTmZ+puSY29TiHQmMpEhi52Tm70qI9XOKO4mfyMw/1L5UnEzRbHt2NrH/aV3M/wGmZ+ZV3da/G/hYZh5WQsxbgQ7g1Mz8c7OP30vMzv7uEylaV18PHAdcXcbfaERcvILNmZlN7wYTEYsoxtsExdifhzs3UXSvHFhCzP1WtD176K7ShJi/Bd7Tywfv/2Q5Yx5bfu2LiH/pYfVAivftZpm5cQkxV3STLDPzH1ewfXVjTl7R9sycWULM5ym6j3bat345M99bQsz68X//CPyS4ibWVplZyhfGiDgJ+GeKVqOzy/6SHBHbAT+haDG/s7Z6HLAH8O6SPjc/v6LtWXJ39laIiPMys6WFTiLifcC/U3TbvJPipuAuFDcmp2bmFSXE3Juii+rF3WJOBj6UmTc3LVYfTXB2pWh+vYSiuxgU/bMnAxMys+k1zCPiVxQtRz8EfkQxyHepMu6+R8QNLLsLnXTta1rWB9FnMvPfmn3cBuK+D3gzxV3Un9XWbQ8c2cqLV0SMoug7/U+ZuW0Jxx+fmcsVMIiILYCvZuaHSoj5ZuAqig+k+r+XPSnvA+nAzLyu2cddhfjrU4wvmEjRBfAN7TqXZoqVVH9qwZebIbU4T5Uc5320/oP3/5XUYtJo/EEUX1SPo/ic+Y/MbPpYy4jYPTNvb/ZxV0dEDKBIZMsoAtLyxLxb/AEUY4AmUYwH/EVmNn0AfkS8RjEm9ym6tlqXWQBpQ4rXtVNt1b3ApVneGK7Kq13ztszM82vLv2ZZ69TUMv5GanHGULT8vZXiPXMvxXeR365wxzWLuQXFeLj6mOc3+2Zon0xwAGpdmU6i+AXBsl/Q/5YUr4OuycbSTRQXkaZXv2qHbt0IllNGV4KI+BbF/+OtFE3D/9OuLxpRVFN7puTuVD3F3YoimSulu2PtA+kouv69lPaB1I73UQ/nsAnwPEU//KYXBVlJ9aLMkgor9HQzoEy1lrHPURSqCIrpBRYD52aJA3Fb/cEbEXdlZmelpnMz8+Mr26dJcTcFTqX4+5wJfCMznysx3tLX2Q7RQ5XDzPxAC+OXeq3tJeYg4IiSWqraetOjdg4DKboITyqpR8APM/Ofao+/nJlT67Zdm5kHNTtmq0XELRTvy8dqy/Mpvg8NBC7Ocqrmtlzt8+vWMm7edNdXx+BQS2RW1H+52fGGtypWp1r3hXMy87Vu6zcD/j0zjysh7BTgHoo7iI9TUpWkbvYFxmTmkoh4HUUhhdITnM4ujRStcV8Avg+8AegXEUdn5jUlx29VaWoy8+/ARWUdvwctfR/VxjL8MDMfqCVz1wBjKL6IT6Io0NFsK6te1PQEp9vNgC9ExK4tuBlwCrA3sEtm/qF2HttQlAL/ZGaeU0bQWiKzwhK4TVb/Ht2rJQGLSl9HUFQOGlVWd+fuYVsQY/mgra9yWB+7JdfaiNgFeKzzTnTtJsj7gUeB08uI2YZxGwBExAYUXYEnUbSWXw5MLyncyLrHB1JUIO1Uytw7bbBBZ3JTc3NmPgM8U0sgm65uPGWPyujGSVEE4/xad+tbKD7LbsnMe5sdqE+24ETEAnoeQBzAa5k5pkXnsS218SKZudPKnr8ax58B7AqclJm31Nb9X4oy0V/PzK+XEHMzig+CCRRfDv8LuLyVdxRbdYcxIuZRFBoYTPEF45DMvD0i3gLMKqM/fvRcmnpClliaOiL+wIrnUyqjK15L30cRcS+wU2ZmRJxI8UXmnRS/45mZuWsZceviB8Ud+KkU1Zu+lCXM1xLFxLddbgZk5tubHadbzN9QlBJ9utv6IcC1Jf2dtPyDt1sLTquuQa8Bf6f4G+mpZ0CvExyuQczn6To2pYuSfrcLgT9SDL6/IjNfjIg/ZGZZRSrada29C3hnZj5bS+hmU7R8jgV2KKOlKopiLpt2tkhFxJ8outMH8OnM7GnOmjWJdyDLWt9+SXFtP7fMm8Ar+ttsd4tks0TEw5n55l62/b6kz+mnKKoozqIoTtHl5keZ3TijmDR6D4qu8nsAwygqvjZtws++2oLz7h7WBcU8KmUPSG/ZZJSZeWJE7AmcV/sC9xaKEpR7ZOYTJcV8huIuzPQoiipMBO6NiKlldbmha/33ALatLZedsK6XmdcCRMSZnf3Sa60AJYXkSYq7l5+luEOTETG+rGA147ot9wP+iWIOl9+UEbAN76NX6roVvguYnZlLgPujqDBUimht9SIoXucSgMxcFCW+Ueus3z25qcV/KsqraLYHK/jgLclb6q4723a7JpUyliEz+zX7mA14ipIm9FyBdlQ5bMe1tn8uG487AZiRmZdTzN80v6SYUyhaUDo9mZlb1sYAXUvPk3KuiZ9R9LLYu65F9xtNjtHd66Ko/NcP2CgiOhOaADYqOXar/DoiTsjM79SvjIiPUryPy7AFRYvYRIrvtFdR3NxtemtKd1kUWdqQ4v9vI2AATf6/7JMJTn2TbESMpTYoHPgDxYW06aJNk1FSdPOZS3EBC+Bfykpu6tUuIBMp3vw/ZVm1lDLs0NMpUH7CWt/1r/sYjbI+fFtemrqWbBAR/SjmiTmNYvK7wzLzvjJjt/B99PcoZr7/X+AAuk6S+7oyAkbX6kUHt6irSG83AwAo40s4xTxVq7NtTbTjg7en61AVvdjbndlat6Omy8x/johTYGmVw68Ar4+If6KkKoe0ZxqA/hGxXhYV095BUTa6U1nft/p1XuNrLgPIokR1GV/+307xe/15RDxC0UrVv4Q49Z6gSMoD+DPF5L+dWlKlswU+CVwREZPoWgxoQ8qZvJ7azbJrgGtqycZE4IbaDd9zy4gZEZ+huIE1hKKk+u0Uk1Sf2Hnzrmmx+mgXte1YVkr4GYom0k9l5goH261hzFcoSjX/S2bOq617JEssLhARH6KYafbbFH/cYyhmT/8dxesto8LOGRQtZPdTXLiuyZLKW/YSfyzdEtbMPK+kWEuAv7LsLlBnX/CgGJhe2lw80drS1OtTzNz9SYqZgs/KzN83O063mC19H9XGU11CcdH8eue4lIg4FPhwZja9lTV6r14ElJNsRMRIirmLHuu2aWvg8cx8ePm91jhm59/Jcpso+e+kFr/zg/crQGkfvD3E3YxifOAfM7PMGzwtFRFLMnO5L6QRMZjipt3+JcQ8uf46Hi2sctjia+2/UoxLeZqiy83OtZajN1N0lW362K7eujbVbmg9XPJ3lL0ofrfvp7hpNifLmepgV4qxTU/UlifXYnYAp2d5c8i1TGdXu4h4B8UE2QD3Zub1JcfdkGJc3ESKiZSvBC7KzD+VFO8Birm3OkuN/zozXyglVh9NcF6jaCI9rvMDvQXJRv0gxc7JKI/JzK1KjPljivlS6lusgqJJ+rQyXm/td/sIy1o0Ot8gpXUXa0fCujaJ8ktTL6To4/91in7wXWQJE1Ku5H1USnefVmtTsvET4DPdx/dExDjg85n5nmbHbJc2fPD+hGJ+tXtqXZHvAuZRzDc0I0sY89gOEXEdcEdm/mvdui0ouh5dniVUxlvROImI2ChLqHLYS6xRFO+nCWVca2sxdgfeRDE+7a+1ddsBG2c5c9d9C3g2Mz/bbf0XgTdk5pRmx+zhHPpRtLgemZnHlnD8lo9tarVozzxcMylKff+Uolv3PS2KuynF2Js9gd2BjYHfUlRXW9H8cqsWp48mOOMpvhDvSdG8Nhv4bpY4YLFb/JZPRtnDOfxTZv6whOP2lFQs7S7WzAFgdTFbnrCuTaLk0tQRcQkrLjJQxoSULS1dGsXM1i9k5oXd1n+col/815sZr3bslicbEXFP9lLQJCIWZOaoZsdsh3Z88EbEvZn51trjzwBvycyjoxisfksVknJYmjheDvwuM0+tJeo/Bb6Smd8uKWbLB4LXWk02z1qBnrr1+1LeDYgBFDcg3wwsAC4suwdEFBW2vktR1bGzhPoYiuT8hMxs6uzwtZjrAUtqrVNbAbsBv8/MUsZ0RsRvO2+uRsT5wFOZeXpteX5mji0jbivVbkR+rbftmdnrtjWI+RrLWudbUuSkW/z1KLrh7Qt8lKKqYtO6O/bJBKdTLKu93jlr8EyKZOPaEmIN7qkZLYrJKL+WJdR+X8n5/DEzh5UcYywt6C7W7oS1lWIFpamBUkpT9/berW3bJTPnNjtmL7FKS+SiqC62c2a+0m39hhSVWcroLtbyZKO37igr29bXtOODt/6LUkT8AvhOZs7uvq0Kal3EZgOvUvSHPyV7mIC4ifEWs6wLcJdNlPf/2Y4bEP9F8Tu9CTgEeDQz/7nZcXqJvQ3L5jm7L0vqhhzFeOQvU3Qz+gLFmM67gLdRtLB+uYSY9wBjM3NxrYvTiZl5Y+e23q7DfUlEPEExXqzHgipZwnjvNrUavZfiu95eFO/Xeym6qt1K0YLTtEmk+2SRgU615t8fAD+oNXl9kGKm66YnOMAvopidvXuZ260p7pa0WilVhXrpLhaZeUAZ8QBqH6xz6hLWTwKbR8QFlJSwttF5LCtNfT3dSlNTJHjN1uN7N4pynxcBTe9muaJELsqZYyi7Jze1lX+vdessw4AVbCurss/c6LnSznGUWwikpbI91cUeq7X4LQR2pva3GMVA7VLHGbVSrbUTispMn6b4Mj6ic30Zd4opJqVt6RcpYHj35AYgM+dFxPCSYu7YeWMjIi6kvOpXS0XEhzLzPzPzkYh4U32LVXQb+9Qkp1B02xxEMcZy68x8Ooqy9XMpkp9mmwX8KiKepuj2fBMsbaUrZfxGGzxRRvfQlWhHC8cxFMnMp4E7e/rcbpY+neDUy2KQ2bdrP2X4NvDL2hfFpwCiqHbxJYo7Na1W1hvzAYqLx3vquot9sqRYXbQ4YW2XdpSmXtF7t6yWx5YnchGxeRYTAHdZ1+w4ddqRbJxCcTPgqLoY44ANKOb80Oo7jqKoyzspxmg8X1u/O9C0fuFrgUF1j7/Zw7qqaMcNiFc7H9RaG0oK08WpwH/WHp9LkZx3+gjFtbiZXqndLHuu1mr8NCwtW1/Kl9XM/FKtVbVzbFPn959+FGNxqqAdE/C+se6Gx3JKutmxcWZ+deVPW3OVSXDKlpnfiYiXgesj4iCKGvdTgAMys6OMmLHiCU3L+uL2fooWnF9GRGd3sZb/4bUgYW2Xlpembsd7l9Yncl8BroqIf6Fric1/p2tJ0WY6hRYnG7UEbs+IOIBijArAVVlypZ11QRZVKZcbkJ2Zv6SY0LASyujq0oDL2hCzHTcgxkTEXzpDUczZ8hfKHdMQvTzuabkZNoplc9JsEF3npFlRUrlGOj9Duq1reiW8NnpHG2L2pxjg38rveKVVTOyuT4/BaYeI+CDFXZI/UtyVfmYlu6xJrJYP+K+L3bLxTeuSaG9p6la+d1s+83REHELR4tf5xf8e4OzM/GmzY3WLW59slF7WU+WIiCtXtD0z39uqcylTRHxuBZszayXWmxzz86y40EkZMTcH5lDM1bTcDYjMrMT8Ka2+1kbELyn+Lzu/FHcZI1dmd3Y1V5uKfzxC13nqusgmVnU1wWlQXWtKUIy7eYplX1RLKZ/cLf5YWjQ/TA+xO7uLTcjMf2xFTDXPSt67WdIA/LYlctLqiIinKEp+zwJ+Tbe7mtnL5Jh9Ta2Vs7uBFF30NsvMjVsU83UUE2aXErMudqVvQETEIuBhivfrtrXH1Ja3ycyBTY5X+Tlp1hVtKjLwDPBjem41ymxiVVcTnAa1ozWllwH/68z8MGqOXt67S2WTSza3Q0R8c0XbM/MTrToX9U0R0Z9iLo+JwGjgKmBWZt7b1hMrURQlsP+ZIrn5IfAfWcIE0u2IGW0o2dwOrb6+xzowJ826IiI2bXVCGnXl+MvmGJwGZdfJNsfSrTWlpLBtG/Cv6ujpAy5KnnunDaZQdEn7IfA47RmwqT4sM5dQFL+4Jory4hOBG2pjyM5t79k1V61V/lTgKIruxzv3UCG0r8ecSdeSzTtQjJurlN4SmFrCfiTQ7BtY/eu+FE+gmAT3cuDyiJjf5FgqUZta2/7eqkAmOA1qR/lk1pIB/+rb2lCyuR3eRK0bJbCY4u/z8rK/tKlaaonNYRTX+eEUVcaa1id8bRARXwGOAGYAozLzpSrGpA0lm9shIl4PnARsCVwJXAecTDHOYT5FZdJm6h8R69Vaw94BnFi3ze+UWpmWTUZtF7UGRTHx3E3AcXWtKY9k5jYtiO2Af622iJjHspLNM+hWsrnVfXDLFhFbUvytnApMzczvt/mU1AdExEyKsRo/BWZn5j1tPqVS1D7L/k5xI6BVk6i2I2ZLipu0W0T8GHgOuI0i4diEopDCP2fm/BLi/StwKPA0MIyiJS5rc9LMzMy9mh1T1RERC4Fey083szS1CU6DImI8RWvKnhTdGGYD383MES0+Dwf8a5VE1xna78/MHeq2tXyQYZlqJUsnUoyluJOif/997T0r9QW1L+F/rS225Eu4ylNX6AS6Fjup1P9nRCyoa6nqTy3xyMwXS4y5O8vmpPlrbd12FHOc3LXCnbVOi4gngAvopTdSM8vYm+CsIltT1Ne0o2Rzq0XEGcC7KWbWng1cU8UBxZJUr6rXdFVTK9+fJjhrwNYU9QXrQsnm2t33R1g2eWqXC1sZpbAlqd3WlZYqVUMre42Y4Ejq8yJiJLA5xTwm9bYGHu8cNydJktqjlaWp+7UiiCSV7BzgL5n5aP0PxZ3Mc9p8bpIkrfNaWZraBEdSFQzPzLu7r8zMeRTlfiVJ0jrCBEdSFQxYwbaNWnYWkiSp7UxwJFXB3Ig4ofvKiDiOoly0JElaR1hkQFKfFxGbA3OAV1iW0IyjmPBufGb+uV3nJkmSWssER1JlRMQBFLPRA9ybmde383wkSVLrmeBIkiRJqgzH4EiSJEmqDBMcSZIkSZVhgiNJkiSpMkxwJEmSJFWGCY4kSZKkyvj/bhJo2NBMQacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "w.plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Adjusted Return:\n",
      "Arithmetic 0.6691999480193228\n",
      "Log Approx 0.6793044757466081\n",
      "Log Exact 0.6794215276607808\n"
     ]
    }
   ],
   "source": [
    "returns = port.returns\n",
    "cov = port.cov\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Risk Adjusted Return:\")\n",
    "print(\"Arithmetic\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Approx\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_3.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_3, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Exact\", (y/x).item() * 12**0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>ALXN</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.3800%</td>\n",
       "      <td>3.6660%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.0581%</td>\n",
       "      <td>5.0469%</td>\n",
       "      <td>20.2861%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>23.9418%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.4723%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.2414%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.8842%</td>\n",
       "      <td>7.1709%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.7818%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.8093%</td>\n",
       "      <td>4.9700%</td>\n",
       "      <td>13.1636%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.8386%</td>\n",
       "      <td>28.7982%</td>\n",
       "      <td>1.3590%</td>\n",
       "      <td>2.9827%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.2928%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>14.1125%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4609%</td>\n",
       "      <td>5.6443%</td>\n",
       "      <td>9.9020%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.5362%</td>\n",
       "      <td>28.9088%</td>\n",
       "      <td>4.1661%</td>\n",
       "      <td>1.5920%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.3290%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>14.1653%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1241%</td>\n",
       "      <td>6.9784%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.5002%</td>\n",
       "      <td>28.8465%</td>\n",
       "      <td>5.9401%</td>\n",
       "      <td>1.1557%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1362%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>13.8230%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.6584%</td>\n",
       "      <td>4.0341%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8218%</td>\n",
       "      <td>28.6600%</td>\n",
       "      <td>7.2207%</td>\n",
       "      <td>0.9650%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.2166%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AIG    AKAM     ALXN     AMT     APA      BA     BAX    BKNG      BMY  \\\n",
       "0 0.0000% 0.3800%  3.6660% 0.0000% 0.0000% 0.0000% 3.0581% 5.0469% 20.2861%   \n",
       "1 0.0000% 0.0000% 12.7818% 0.0000% 0.0000% 0.0000% 0.8093% 4.9700% 13.1636%   \n",
       "2 0.0000% 0.0000% 14.1125% 0.0000% 0.0000% 0.0000% 0.4609% 5.6443%  9.9020%   \n",
       "3 0.0000% 0.0000% 14.1653% 0.0000% 0.0000% 0.0000% 0.0000% 6.1241%  6.9784%   \n",
       "4 0.0000% 0.0000% 13.8230% 0.0000% 0.0000% 0.0000% 0.0000% 6.6584%  4.0341%   \n",
       "\n",
       "    CMCSA  ...    NTAP    PCAR      PSA    REGN    SBAC     SEE       T  \\\n",
       "0 0.0000%  ... 0.0000% 0.0000% 23.9418% 0.0000% 5.4723% 0.0000% 0.2414%   \n",
       "1 0.0000%  ... 0.0000% 0.8386% 28.7982% 1.3590% 2.9827% 0.0000% 0.0000%   \n",
       "2 0.0000%  ... 0.0000% 1.5362% 28.9088% 4.1661% 1.5920% 0.0000% 0.0000%   \n",
       "3 0.0000%  ... 0.0000% 2.5002% 28.8465% 5.9401% 1.1557% 0.0000% 0.0000%   \n",
       "4 0.0000%  ... 0.0000% 3.8218% 28.6600% 7.2207% 0.9650% 0.0000% 0.0000%   \n",
       "\n",
       "      TGT     TMO    TTWO  \n",
       "0 0.0000% 1.8842% 7.1709%  \n",
       "1 0.0000% 0.0000% 7.2928%  \n",
       "2 0.0000% 0.0000% 6.3290%  \n",
       "3 0.0000% 0.0000% 6.1362%  \n",
       "4 0.0000% 0.0000% 6.2166%  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 40 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, kelly=\"exact\", points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdbf51d5460>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Log Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plf.plot_frontier(w_frontier=frontier,\n",
    "                       mu=mu,\n",
    "                       cov=cov,\n",
    "                       returns=returns,\n",
    "                       rm=rm,\n",
    "                       kelly=True,\n",
    "                       rf=rf,\n",
    "                       alpha=0.05,\n",
    "                       cmap='viridis',\n",
    "                       w=w_3,\n",
    "                       label=label,\n",
    "                       marker='*',\n",
    "                       s=16,\n",
    "                       c='r',\n",
    "                       height=6,\n",
    "                       width=10,\n",
    "                       t_factor=12,\n",
    "                       ax=ax)\n",
    "\n",
    "y1 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy())) * 12\n",
    "x1 = rk.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 12**0.5\n",
    "\n",
    "y2 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy())) * 12\n",
    "x2 = rk.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 12**0.5\n",
    "\n",
    "ax.scatter(x=x1,\n",
    "           y=y1,\n",
    "           marker=\"^\",\n",
    "           s=8**2,\n",
    "           c=\"b\",\n",
    "           label=\"Max Risk Adjusted Arithmetic Return Portfolio\")\n",
    "ax.scatter(x=x2,\n",
    "           y=y2,\n",
    "           marker=\"v\",\n",
    "           s=8**2,\n",
    "           c=\"c\",\n",
    "           label=\"Max Risk Adjusted Approx Log Return Portfolio\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Logarithmic Mean EDaR Portfolios\n",
    "\n",
    "### 3.1 Calculating the portfolio that maximizes Risk Adjusted Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arithmetic</th>\n",
       "      <th>Log Approx</th>\n",
       "      <th>Log Exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALXN</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.1824%</td>\n",
       "      <td>3.1120%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT</th>\n",
       "      <td>2.8669%</td>\n",
       "      <td>0.1277%</td>\n",
       "      <td>0.2234%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0005%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0002%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAX</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0007%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKNG</th>\n",
       "      <td>3.0492%</td>\n",
       "      <td>0.7226%</td>\n",
       "      <td>0.7547%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0009%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0004%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0002%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0007%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0003%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>27.1688%</td>\n",
       "      <td>31.1116%</td>\n",
       "      <td>31.4365%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0017%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NI</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0003%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTR</th>\n",
       "      <td>12.5570%</td>\n",
       "      <td>11.8591%</td>\n",
       "      <td>12.2503%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTAP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCAR</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.2472%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>25.6707%</td>\n",
       "      <td>36.2462%</td>\n",
       "      <td>32.9942%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGN</th>\n",
       "      <td>19.7835%</td>\n",
       "      <td>8.9458%</td>\n",
       "      <td>11.1137%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBAC</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.9234%</td>\n",
       "      <td>2.4986%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0002%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0002%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0003%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0313%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTWO</th>\n",
       "      <td>8.9039%</td>\n",
       "      <td>4.5958%</td>\n",
       "      <td>5.6165%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arithmetic  Log Approx  Log Exact\n",
       "AIG       0.0000%     0.0000%    0.0000%\n",
       "AKAM      0.0000%     0.0001%    0.0000%\n",
       "ALXN      0.0000%     4.1824%    3.1120%\n",
       "AMT       2.8669%     0.1277%    0.2234%\n",
       "APA       0.0000%     0.0005%    0.0000%\n",
       "BA        0.0000%     0.0002%    0.0000%\n",
       "BAX       0.0000%     0.0007%    0.0000%\n",
       "BKNG      3.0492%     0.7226%    0.7547%\n",
       "BMY       0.0000%     0.0009%    0.0000%\n",
       "CMCSA     0.0000%     0.0004%    0.0000%\n",
       "CNP       0.0000%     0.0002%    0.0000%\n",
       "CPB       0.0000%     0.0007%    0.0000%\n",
       "DE        0.0000%     0.0003%    0.0000%\n",
       "MO       27.1688%    31.1116%   31.4365%\n",
       "MSFT      0.0000%     0.0017%    0.0000%\n",
       "NI        0.0000%     0.0003%    0.0000%\n",
       "NKTR     12.5570%    11.8591%   12.2503%\n",
       "NTAP      0.0000%     0.0001%    0.0000%\n",
       "PCAR      0.0000%     0.2472%    0.0000%\n",
       "PSA      25.6707%    36.2462%   32.9942%\n",
       "REGN     19.7835%     8.9458%   11.1137%\n",
       "SBAC      0.0000%     1.9234%    2.4986%\n",
       "SEE       0.0000%     0.0002%    0.0000%\n",
       "T         0.0000%     0.0002%    0.0000%\n",
       "TGT       0.0000%     0.0003%    0.0000%\n",
       "TMO       0.0000%     0.0313%    0.0000%\n",
       "TTWO      8.9039%     4.5958%    5.6165%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm = 'EDaR' # Risk measure\n",
    "\n",
    "w_1 = port.optimization(model=model, rm=rm, obj=obj, kelly=False, rf=rf, l=l, hist=hist)\n",
    "w_2 = port.optimization(model=model, rm=rm, obj=obj, kelly='approx', rf=rf, l=l, hist=hist)\n",
    "w_3 = port.optimization(model=model, rm=rm, obj=obj, kelly='exact', rf=rf, l=l, hist=hist)\n",
    "\n",
    "w = pd.concat([w_1, w_2, w_3], axis=1)\n",
    "w.columns = ['Arithmetic', 'Log Approx', 'Log Exact']\n",
    "\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "w.plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Adjusted Return:\n",
      "Arithmetic 0.8286211415998024\n",
      "Log Approx 0.846070950283653\n",
      "Log Exact 0.8477024914731162\n"
     ]
    }
   ],
   "source": [
    "returns = port.returns\n",
    "cov = port.cov\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Risk Adjusted Return:\")\n",
    "print(\"Arithmetic\", (y/x).item() * 12)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Approx\", (y/x).item() * 12)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_3.to_numpy()))\n",
    "x = rk.Sharpe_Risk(w_3, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Exact\", (y/x).item() * 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>ALXN</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.9566%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.0827%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7697%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.0086%</td>\n",
       "      <td>28.4287%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.1175%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.4921%</td>\n",
       "      <td>39.8202%</td>\n",
       "      <td>1.8910%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.6297%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.2596%</td>\n",
       "      <td>0.8573%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6313%</td>\n",
       "      <td>36.5955%</td>\n",
       "      <td>8.1015%</td>\n",
       "      <td>0.5015%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4582%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.2296%</td>\n",
       "      <td>0.2013%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7193%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>33.1989%</td>\n",
       "      <td>10.9634%</td>\n",
       "      <td>2.4576%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.5881%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7741%</td>\n",
       "      <td>0.6800%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.4780%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>28.9304%</td>\n",
       "      <td>14.1497%</td>\n",
       "      <td>3.2846%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.2098%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AIG    AKAM    ALXN     AMT     APA      BA     BAX    BKNG     BMY  \\\n",
       "0 0.0000% 0.0000% 4.9566% 0.0000% 7.0827% 0.0000% 0.0000% 0.7697% 0.0000%   \n",
       "1 0.0000% 0.0000% 9.1175% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000%   \n",
       "2 0.0000% 0.0000% 5.2596% 0.8573% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000%   \n",
       "3 0.0000% 0.0000% 3.2296% 0.2013% 0.0000% 0.0000% 0.0000% 0.7193% 0.0000%   \n",
       "4 0.0000% 0.0000% 0.7741% 0.6800% 0.0000% 0.0000% 0.0000% 1.4780% 0.0000%   \n",
       "\n",
       "    CMCSA  ...    NTAP    PCAR      PSA     REGN    SBAC     SEE       T  \\\n",
       "0 0.0000%  ... 0.0000% 1.0086% 28.4287%  0.0000% 0.0000% 0.0000% 0.0000%   \n",
       "1 0.0000%  ... 0.0000% 1.4921% 39.8202%  1.8910% 0.0000% 0.0000% 0.0000%   \n",
       "2 0.0000%  ... 0.0000% 0.6313% 36.5955%  8.1015% 0.5015% 0.0000% 0.0000%   \n",
       "3 0.0000%  ... 0.0000% 0.0000% 33.1989% 10.9634% 2.4576% 0.0000% 0.0000%   \n",
       "4 0.0000%  ... 0.0000% 0.0000% 28.9304% 14.1497% 3.2846% 0.0000% 0.0000%   \n",
       "\n",
       "      TGT     TMO    TTWO  \n",
       "0 0.0000% 0.0000% 0.0000%  \n",
       "1 0.0000% 0.0000% 3.6297%  \n",
       "2 0.0000% 0.0000% 4.4582%  \n",
       "3 0.0000% 0.0000% 5.5881%  \n",
       "4 0.0000% 0.0000% 6.2098%  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 40 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, kelly=\"exact\", points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdbf51d5f40>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Log Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plf.plot_frontier(w_frontier=frontier,\n",
    "                       mu=mu,\n",
    "                       cov=cov,\n",
    "                       returns=returns,\n",
    "                       rm=rm,\n",
    "                       kelly=True,\n",
    "                       rf=rf,\n",
    "                       alpha=0.05,\n",
    "                       cmap='viridis',\n",
    "                       w=w_3,\n",
    "                       label=label,\n",
    "                       marker='*',\n",
    "                       s=16,\n",
    "                       c='r',\n",
    "                       height=6,\n",
    "                       width=10,\n",
    "                       t_factor=12,\n",
    "                       ax=ax)\n",
    "\n",
    "y1 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy())) * 12\n",
    "x1 = rk.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "\n",
    "y2 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy())) * 12\n",
    "x2 = rk.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "\n",
    "ax.scatter(x=x1,\n",
    "           y=y1,\n",
    "           marker=\"^\",\n",
    "           s=8**2,\n",
    "           c=\"b\",\n",
    "           label=\"Max Risk Adjusted Arithmetic Return Portfolio\")\n",
    "ax.scatter(x=x2,\n",
    "           y=y2,\n",
    "           marker=\"v\",\n",
    "           s=8**2,\n",
    "           c=\"c\",\n",
    "           label=\"Max Risk Adjusted Approx Log Return Portfolio\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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